Hosted by: Mark Hybertsen

Advances in scanning transmission electron and scanning probe microscopies have recently allowed researchers to measure materials structural and electronic properties, such as atomic displacements and charge density modulations, at a picometer scale in real space. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large data sets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. In this seminar, I discuss our recent progress in applying big-data analytical tools, such as multivariate statistical analysis and machine learning, to structural and functional imaging of quantum materials. Specifically, I focus on applications of these tools to scanning probe and transmission electron microscopy experiments on graphene,1 Kitaev quantum spin liquid candidate alpha-RuCl3,2,3 and films of buckybowl molecules.4 References: 1. Ziatdinov, M.; Dyck, O.; Maksov, A.; Li, X.; Sang, X. et al. ACS Nano 2017, doi: 10.1021/acsnano.7b07504. 2. Ziatdinov, M.; Banerjee, A.; Maksov, A.; Berlijn, T.; Zhou, W. et al. Nature Communications 2016, 7, 13774. 3. Vasudevan, R. K.; Ziatdinov, M.; Jesse, S.; Kalinin, S. V. Nano Letters 2016, 16, 5574-5581. 4. Ziatdinov, M.; Maksov, A.; Kalinin, S. V. npj Computational Materials 2017 3, 31.